acoustic monitoring reveals the times and tides of · 2020-01-27 · acoustic monitoring reveals...

Acoustic monitoring reveals the times and tides ofharbor porpoise (Phocoena phocoena)distribution off central Oregon, U.S.A.

AMANDA K. HOLDMAN,1,2 Marine Mammal Institute, Department of Fisheriesand Wildlife, Oregon State University, Hatfield Marine Science Center, 2030, SEMarine Science Drive, Newport, Oregon, 97365, U.S.A. and CooperativeInstitute for Marine Resources Studies, Oregon State University and NOAAPacific Marine Environmental Laboratory, Hatfield Marine Science Center, 2030,SE Marine Science Drive, Newport, Oregon, 97365, U.S.A.; JOSEPH H. HAXEL,Cooperative Institute for Marine Resources Studies, Oregon State University andNOAA Pacific Marine Environmental Laboratory, Hatfield Marine ScienceCenter, 2030, SE Marine Science Drive, Newport, Oregon, 97365, U.S.A.;HOLGER KLINCK, Bioacoustics Research Program, Cornell Laboratory ofOrnithology, Cornell University, 159 Sapsucker Woods Road, Ithaca, New York,14850, U.S.A.; LEIGH G. TORRES , Marine Mammal Institute, Department ofFisheries and Wildlife, Oregon State University, Hatfield Marine Science Center,2030 SE Marine Science Drive, Newport, Oregon, 97365, U.S.A.

ABSTRACT

Harbor porpoises (Phocoena phocoena) are commonly observed inOregon’s nearshore marine environment yet knowledge of their eco-system use and behavior remains limited, generating concerns forpotential impacts on this species from future coastal development. Pas-sive acoustic monitoring was used to investigate spatial and temporalvariations in the presence and foraging activity of harbor porpoises offthe Oregon coast from May through October 2014. Digital monitoringdevices (DMONs) were deployed to record acoustic data (320 kHz sam-ple rate) in two neighboring but bathymetrically different locations offthe Oregon coast: (1) a site on the 30 m isobath in close proximity(<50 m) to a rocky reef, and (2) a site on the 60 m isobath in an opensandy environment. Data were analyzed with respect to two dynamiccyclic variables: diel and tidal phase. Porpoise presence at the rockyreef site was aligned with the ebb phase of the tidal forcing, while, har-bor porpoise presence and foraging at the offshore, sandy bottom sitewas associated with night-time foraging. The spatial and temporal pat-terns identified in this study suggest harbor porpoise habitat use is

1Corresponding author (e-mail: [email protected]).2Current address: University of Alabama at Birmingham, 1300 University Boulevard,Birmingham, Alabama 35294, U.S.A.

MARINE MAMMAL SCIENCE, 35(1): 164–186 (January 2019)© 2018 The Authors. Marine Mammal Science published by Wiley Periodicals, Inc. onbehalf of Society for Marine Mammalogy.This is an open access article under the terms of the Creative Commons AttributionLicense, which permits use, distribution and reproduction in any medium, provided theoriginal work is properly cited.DOI: 10.1111/mms.12537

164

modulated by specific environmental conditions particular to each sitethat maximize foraging efficiency.

Key words: harbor porpoise, Phocoena phocoena, distribution, habitatuse, foraging behavior, temporal patterns, Oregon, passive acoustics.

A combination of escalating pressures related to human activities isthreatening almost all marine ecosystems worldwide (Halpern et al.2008, Maxwell et al. 2013). Cetaceans are particularly vulnerable to inci-dental harm from anthropogenic activities, such as fisheries, noise andchemical pollution, shipping, and habitat loss, because they are long-lived with low fecundity (Heppell et al. 2005). Human activities vary intheir spatial and temporal distribution across regions resulting in a widerange of ecological impacts for cetacean populations (Halpern et al.2008). In particular, cetaceans that live in near-shore, shallow waterenvironments are often exposed to elevated levels of anthropogenicactivities (Barlow and Forney 1994). Furthermore, baseline informationon the distribution and habitat usage of coastal species is often limited.Further, existing data are often only available at spatial and temporalscales much larger than what is needed to adequately inform regulatorydecisions for fine-scale activities, including marine energy, seabed min-ing and point source pollution (Forney et al. 2017). Knowledge of ceta-cean distributions and habitat use at finer, site-specific scales is crucialto answer management questions and capture relevant habitat heteroge-neity (Wiens 1989, Tett et al. 2013).High-resolution spatial habitat-use data for marine mammals can be

obtained through aerial or boat based visual surveys, with considerablecost and effort (Evans and Hammond 2004). Visual surveys typicallyhave a wide spatial extent and are capable of covering a site-specificarea, but are limited in temporal coverage to daylight hours and reason-able weather conditions. Furthermore, visual surveys are reliant on ani-mals being identified at the surface, but cetaceans are often visible at thesurface less than 10% of the time (Tyack and Miller 2002), limiting thevisual detection time of observers. Passive acoustic monitoring (PAM)provides an alternate survey technique that can be used to examine pat-terns of movement and trends in behavior of vocalizing animals(Carlström 2005, Todd et al. 2009). Fixed passive acoustic recorders candetect vocalizing marine mammals during all hours, seasons, and seastates (Mellinger et al. 2007). Also, PAM allows for subsurface detection,is noninvasive, and unlikely to affect cetacean behavior, all the whileproviding information on animal presence and behavior at high tempo-ral resolution.In studying marine mammal distribution and foraging patterns, direct

interactions with prey are often difficult to observe. In their absence, anindirect understanding of marine mammals and their prey can be benefi-cial. At fine temporal scales (<1 d) consistent diel and tidal patternsoccur, which influence everything from primary production (Zamon2002, 2003; Sharples et al. 2007) to marine top predators (Baumgartneret al. 2003, Hastie et al. 2004, Wang et al. 2015). Additionally, at fine

HOLDMAN ET AL.: HARBOR PORPOISE DISTRIBUTION AND HABITAT USE 165

spatial scales (1–10 km), oceanic processes (upwelling, fronts, andeddies) can enhance biological production and consequently aggregatezooplankton (Scott et al. 2010). These fine-scale patterns in both timeand space act to localize patches of food for marine predators. Further-more, tidal currents can interact with bathymetry and aggregate preyvertically and horizontally (Zamon 2002), which results in spatially andtemporally predictable prey patches (Riley 1976), which may attractmarine predators (Hastie et al. 2004, Johnston et al. 2005). Preferenceby top predators for these brief yet predictable areas may be undetect-able in larger-scale surveys, and failure to account for the distributionsand habitat use of marine predators at fine spatial and temporal scales(<1 km, hours) may mask behavioral changes in response to anthropo-genic disturbances.New marine spatial planning and conservation initiatives have

increased the need for finer-scale data regarding cetacean use of coastaland shelf waters (Rees et al. 2013). In the Pacific Northwest, the marineenvironment off Oregon has been considered for coastal development inrecent years through potential development of wind and wave energyconverters, vessel traffic, and fishing activities (Callaway 2007, Boehlertet al. 2008). The cumulative impact of a multitude of threats requiresmanagers to understand how these activities affect the marine environ-ment at fine-scales. Consequently, there is a need to provide managersand stakeholders with local population level information on species ofconcern within the management area. Although cryptic, harbor por-poises (Phocoena phocoena) are readily found along the Oregon coastand are a focal at-risk species (Henkel et al. 2014) that is highly sensitiveto anthropogenic noise (Lucke et al. 2009; Tougaard et al. 2009, 2012,2015; Dyndo et al. 2015). Harbor porpoises appear to be susceptible toauditory injuries at much lower levels than other studied cetaceans(Lucke et al. 2009, Tougaard et al. 2015), and if close enough to high-intensity sounds, harbor porpoise may suffer temporary or even perma-nent hearing loss as a result of exposure to human noise (Lucke et al.2009, Kastelein et al. 2012). However, porpoises may also be threatenedby noise at much larger distances from the sound source throughresponses such as increased stress levels (Wright et al. 2007), and behav-ioral changes (Richardson and Würsig 1997). In addition, noise expo-sure can lead to constrained acoustic communication through auditorymasking (Kastelein et al. 2011) and broad-scale spatial displacement(Culik et al. 2001, Teilmann et al. 2008, Tougaard et al. 2009).Harbor porpoises along the west coast of North America are predomi-

nately observed in coastal waters <200 m deep (Minasian et al. 1984,Barlow et al. 1988, Carretta et al. 2001), and two stocks are currentlyrecognized in Oregon (a northern and southern population with a sepa-ration area located near Lincoln City; Carretta et al. 2011). Seasonalchanges in abundance along the west coast have been reported with alower abundance during the winter (Barlow et al. 1988). Although itremains unclear whether reported movement patterns are related to sea-sonal inshore-offshore movements or because observation conditionsare poor and it is harder to survey and detect them. Harbor porpoisepopulations in other regions display habitat-use patterns relative to

166 MARINE MAMMAL SCIENCE, VOL. 35, NO. 1, 2019

season (Simon et al. 2010, Gilles et al. 2011), diel cycle (Carlström 2005,Todd et al. 2009, Schaffeld et al. 2016), and tides (Johnston et al. 2005,Pierpoint 2008, Benjamins et al. 2016). However, the influence from dieland tidal cycles are region-specific, and this is likely related to the avail-ability of dominant prey in the area. Harbor porpoises have high energydemands, due to their small body size and typically temperate waterhabitat, necessitating daily foraging to meet their basic energy require-ments (Koopman 1998, Wisniewska et al. 2016). Porpoises feed at adaily rate of 10% of their body weight (Read and Gaskin 1985), and theirdistribution and movements are believed to be strongly connected topatches of prey aggregations (Sveegaard et al. 2012).Studying the distribution patterns of harbor porpoises using visual

methods has proven difficult due to their small size, small group size,cryptic behavior and minimal surface activity, as they are visible at thesurface for less than 25% of the time (Laake et al. 1997). Furthermore,they are difficult to observe when sea conditions deteriorate above aBeaufort Sea State of 2 (Teilmann 2003), and photo-identification effortsare challenging due to a general lack of distinctive, natural markings ontheir dorsal fins (Koopman and Gaskin 1994). However, harbor por-poises are highly vocal animals and are thought to echolocate almostcontinuously (Villadsgaard et al. 2007, Linnenschmidt et al. 2013, Wis-niewska et al. 2016), making passive acoustic surveys often a more suc-cessful approach for distribution and behavioral observations thanvisual methods (Teilmann 2003, Kyhn et al. 2008).Harbor porpoise produce narrow-band, high-frequency echolocation

clicks with a peak frequency of 130 kHz (Møhl and Andersen 1973, Vil-ladsgaard et al. 2007). The mean source level (SL) has been estimated at191 dB re 1 μPa p-p @ 1 m, ranging from 178 to 205 dB re 1 μPa p-p(Villadsgaard et al. 2007). Additionally, there is typically no energybelow 100 kHz (Kyhn et al. 2012), enabling harbor porpoise clicks to bereliably discriminated from other odontocete (e.g., delphinid) signals, aswell as most other transient sounds. Harbor porpoise produce echoloca-tion clicks for communicating, foraging, and navigation (Verfuß et al.2005, 2009; Clausen et al. 2011). Foraging click trains (series of clicks)can be separated into different phases based on the interclick interval(ICI): the search phase is characterized by relatively stable ICIs around50 ms, and the terminal phase is identified by a sudden and rapid short-ening of ICI to levels below 10 ms (Linnenschmidt et al. 2012). Echolo-cation clicks that transition into a foraging click train with short andstable ICI of below 10 ms are called “buzzes” (DeRuiter et al. 2009,Verfuß et al. 2009, Madsen et al. 2013) and consequently buzzesrecorded by acoustic data loggers have been used as a reliable proxy offoraging efficiency (Miller et al. 2004, Linnenschmidt et al. 2013, Wis-niewska et al. 2016).In order to obtain fine-scale data on harbor porpoise occurrence and

foraging patterns in coastal Oregon waters, two passive acoustic moni-toring devices (DMON, Baumgartner et al. 2013) were deployed andoperated for an extended period. Unlike traditionally applied staticacoustic recorders such as T-PODs and C-PODs (Chelonia Limited,Mousehole, U.K.) that employ an onboard, automated call detection


approach (Leeney et al. 2011, Wang et al. 2015), DMONs record fullspectral waveforms within the high-frequency vocal range of harbor por-poise. T-POD and C-POD devices record continuously and rely on theperformance of an algorithm to detect and classify click trains into cate-gories (narrow-band, high-frequency clicks and nonnarrow band high-frequency clicks). In contrast, DMONs archive the acoustic data (typi-cally on a duty cycle), which allows for supervised, visual detection andclassification of echolocation activity by an analyst, leading to morerobust estimates of true occurrence and behavior (Roberts and Read2015). However, the DMON version used for this experiment is not wellsuited for long term studies due to limited battery and data storagecapacity. In this study, we explore the influence of dynamic, fine-scale,cyclic environmental variables (tidal and diel forcing) on harbor por-poise distribution and foraging patterns at two neighboring but bathy-metrically distinct habitats (nearshore reef vs. offshore sandy bottom).

MATERIALS AND METHODS

Study Sites and Instrument Deployments

DMONs were deployed at sites off the central Oregon coast for a 6 moperiod from 13 May to 14 October 2014. Owing to the high sample rate(320 kHz) required for capturing harbor porpoise vocalizations, therecorders were programmed to record on a 10% duty cycle (first minute ofevery 10 min period) to conserve both battery power and memory storagespace. The system features a noise floor 32 dB re μPa/√Hz and a systemsensitivity of −203 dB re V/μPa (Baumgartner et al. 2013). The DMON wasmounted with positively buoyant housing to avoid interference and sus-pended ~5 m above the seafloor along a mooring line attached to a surfacebuoy. The two instruments were operated on the 30 m isobath in closeproximity (<50 m) to a rocky reef and offshore on the 60 m isobath in anopen sandy environment (Fig. 1). The reef and offshore sites were located4 km and 12 km southwest of the Yaquina River inlet, respectively. Indi-vidual deployments were approximately two weeks in duration, limited byDMON battery and data storage capacity. Independence between moor-ings was assumed given the intermooring distance of >8 km.

Acoustic Data Analysis

Data from the DMONs were offloaded via USB and were visuallyreviewed by an analyst using the MATLAB-based software package Tritondeveloped by the Scripps Whale Acoustics Lab, San Diego, CA (Wigginsand Hildebrand 2007). All spectrograms were calculated with 1,024-pointfast Fourier transform (FFT) with 50% overlap and a Hann window. Thedetection ranges for DMONs have not been investigated for harbor por-poises. However, estimates for other passive acoustic monitoring devicessuch as the T-POD are a few hundred meters (Villadsgaard et al. 2007;Kyhn et al. 2008, 2012; DeRuiter et al. 2010). All DMON data were ana-lyzed at a temporal resolution of 1 min, with each minute classified as1 or 0, denoting the presence or absence of harbor porpoise echolocation


click trains and buzzes. While Dall’s porpoises (Phocoenoides dalli) alsooccur along the U.S. West Coast and produce very similar narrow-band,high-frequency echolocation signals, we are confident that our data setincludes only harbor porpoise echolocation click trains because Dall’sporpoises are typically found in much deeper offshore waters (Forney2000). Previous studies have used a group of clicks separated by 10 minto define a separate encounter (Carlström 2005); in this study, each sur-veyed minute is a potential new encounter and not a continuation of theprevious detection. A harbor porpoise encounter was defined as anyrecording minute that contained at least five visually confirmed clicks,and termed a porpoise positive minute (PPM). Click bouts consisting ofless than 5 clicks were discarded from further analysis. In addition topresence patterns, individual harbor porpoise click trains were analyzedfor feeding behavior through assessment of the ICI, which was used todifferentiate between feeding buzz trains and all other trains. A minimumICI (MICI) of <10 ms was used to identify terminal buzz vocalizations, aproxy of porpoise feeding (Carlström 2005, Todd et al. 2009). A click trainthat progressed into an ICI of <10 ms had to be recorded for the sequenceto be recorded as a terminal buzz positive minute (BPM).

Temporal Patterns of Site Use

To analyze PPMs across the study period, a percent daily detectionwas calculated (PPM/Day) for the reef site and offshore site. Due to duty

Figure 1. Bathymetric overview of study area in coastal Oregon (see inset)with acoustic instrumentation deployment sites displayed by the black dots.


cycling, a maximum number of 144 1 min data files could be recordedper day. Sampling effort across the study period varied in relation to ves-sel availability and how quickly DMONs could be recovered, refur-bished, and redeployed. When devices were deployed at both the reefsite and offshore site during the same time period, detections were com-pared to determine whether porpoises were selecting between the twolocations during certain environmental conditions or spending equaltime at both sites. For each synchronous recording period, a numberwas assigned to represent a discrete state of presence or absence forboth sites. At the reef site, each minute of recording was assigned a onefor presence or a zero for absence. At the offshore site, each minute ofrecording was assigned a two for presence or a zero for absence. There-fore, for each time period of acoustic monitoring overlap between sites,a sum of one represented a detection at the reef only, a sum of tworepresented a detection at the offshore site only, and a sum of threerepresented porpoises detected at the same time at both sites. Finally asum of zero represented an absence of porpoises at both sites and weremoved these absences from further analysis. For the remainingperiods with porpoise detections we used contingency tables to comparethe proportion of time the detections occurred at either the reef or off-shore site to that when detections occurred at the same time at bothsites.

Diel Phase Classification and Analysis

Harbor porpoise occurrence and presumed foraging patterns wereinvestigated based on the change in PPM between diel phases of thediurnal cycle. Porpoise detections from each site were classified intofour diel phases (morning, day, evening, and night) according to localcivil twilight and sun-state tables obtained from the U.S. Naval Observa-tory (http://aa.usno.navy.mil). Definitions of diel phases were adaptedfrom Carlström (2005): civil twilight start and civil twilight end refers tothe time point in the morning and the evening where the center of thesun is geometrically 6� below the horizon. Sunrise and sunset refers tothe times when the upper edge of the disk of the sun was on the hori-zon (see fig. 3 in Todd et al. 2009). Porpoise detection rates were com-puted as the proportion of PPMs relative to the whole period ofinvestigation and diel phase. To investigate the proportion of time por-poises spent foraging, we calculated the percentage of PPMs that con-tained a terminal buzz (BPM) for each diel phase at each site. All traindetections were nonnormally distributed (Shapiro-Wilk, P = 2.2e−16).Therefore, nonparametric Kruskal-Wallis one way ANOVAs and theirappropriate post hoc tests, corrected for multiple comparisons, wereused to assess differences in PPMs and BPMS between the diel phases ateach site. All statistical analyses were carried out using the program R(R Development Core Team 2010).

Tidal Phase Classification and Analysis

To examine tidal influences on detection rates, the time dependenttidal phase (φt) was compared with PPM and BPM at each site. Due to


the complex nature of the mixed semidiurnal tides and range of ampli-tudes experienced in the study areas (−0.179–2.934 m, NOAA station9435380), a simple comparison of the nearby-measured hydrostatic tidalamplitude is insufficient for determining linkages between barotropictidal currents and harbor porpoise presence and behavior. Rather, usinga calculated tidal phase parameter (ϕt) we make a more direct compari-son of the velocity of tidally influenced currents relative to harbor por-poise presence and behavior. In this region of the northeast Pacific, thetemporal dependency of the tidal phase parameter (ϕt) is dominated bythe principal lunar semidiurnal constituent (M2), which has a period of12 h and 25.2 min, exactly half a tidal lunar day. ϕt is calculated as thetime dependent 2π modulus of the radial frequency of the tidal constitu-ent M2 plus a constant phase delay C (Pond and Pickard 1983). Thisapproach provides a direct link to dynamic physical processes related totidally induced flow speeds and direction within each habitat, not justthe changes in hydrostatic water levels.Due to the cyclical nature of time and tides, we used the circular statis-

tics toolbox, CircStat for MATLAB (Batschelet 1981, Berens 2009), toanalyze PPM and BPM time series from each site with respect to thesemidiurnal tidal phase. Temporal occurrences of PPM and BPM weretransformed to angular values to describe their distribution relative totidal phase. A Rayleigh test of uniformity was implemented to determineif the null hypothesis that the tidal phases of PPM and BPM were uni-formly distributed, could be rejected. A Hartigan’s dip test (Hartigan andHartigan 1985) was performed to determine whether the distributionswere unimodal or multimodal with respect to tidal phase.

Temporal Model

PPM and BPM were further analyzed and modeled using binomialgeneralized additive models (GAM) with a logit link function withrespect to three temporal variables: Julian day, time of day, and tidalphase, along with their interactions. Data from the two sites were mod-eled separately to assess the effect of temporal variables for each siteand each behavior. Tidal phase was determined by the tidal frequencyof the M2 constitute. GAMs were generated in R package MGCV (Wood2006), which contains integrated smoothness estimation. Nonlinearinteractions in the model structure were allowed in order to capture anychanges in preferences for one covariate as a function of another.Smooth functions for model covariates were specified using thin plateregression splines with shrinkage (Wood 2006). Interactions betweencovariates were modeled using tensor product (te) smooths. Hour of daywas modeled with cyclic smoothers to account for the circular nature oftime. In order to select the model that explained the most variationusing the fewest number of variables, predictor variables were removedone at a time through manual backwards stepwise selection by remov-ing variables not significantly influencing the model outcome. Themodel fit score Akaike’s information criterion (AIC, Akaike 1973) wasused to select the best model at each step. The AIC score must bereduced by a value of 2 or more for a covariate to be considered for


removal from the model. This method was repeated until no covariatescould be removed from the model based on reduction of AIC.

RESULTS

Distributions Across Study Period

Ten total deployments were made over the 6 mo deployment period:five at each site at a variable rate of one to two deployments per month(Table 1). Moored DMONs collected approximately 43 d of acoustic dataat the reef site and 60 d at the offshore site. This effort included 35 d ofdeployment overlap allowing for site comparison. DMONs at both siteslogged data as programmed throughout deployments. During the fifthdeployment, the DMON at the reef site was accidently dragged by fisher-man shortly after deployment.Harbor porpoise were acoustically detected on 96% and 93% of the

total monitored days at the reef and offshore site respectively. All but5 d had at least one detection of a harbor porpoise encounter acrossboth locations. Peak harbor porpoise detections occurred between themonths of June and July with a gradual decreasing trend in monthlypresence through the fall, with the lowest PPMs in October (Fig. 2). Thelargest daily peak occurred in September with almost 70% PPM detec-tion on the offshore station. During the entire 6 mo deployment period,a total of 13,318 (5,520 at the reef and 7,798 offshore) monitoredminutes of the combined 2 sites resulted in 3,477 (26%) PPM. In 27%(964) of all PPM, foraging behavior was detected as defined by an MICI< 10 ms; this constitutes 7.2% of all analyzed minutes with buzz content.Click train detection rates were higher at the reef site (2,057 of 5,520;

38%) compared to the offshore site (1,420 of 7,798; 18%). Relative forag-ing activity was a little higher, although not statistically significant, atthe reef site where 30% of click trains were classified as buzzes (611 of2,057) compared to 25% offshore (353 of 964).DMONs were deployed at both the offshore site and the reef site dur-

ing the same time period for a total of 4,461 min sampled, allowing forcomparison between sites (Table 3). Of the monitored minutes whenDMONs were recording simultaneously, 2,332 PPM and 796 BPM wererecorded across both sites (Table 3). During 78% of these comonitoredminutes, PPM occurred at either the offshore or the reef site, comparedto 22% when PPMs were detected at both sites simultaneously. Whenboth DMONs were recording, only 5% of BPMs were simultaneouslydetected at both sites compared to 95% of recordings where a BPMoccurred at either the offshore site only or the reef site only.

Diel Patterns

The variation in the PPM showed significant difference across thefour diel phases for the reef (Kruskal-Wallis ANOVA on ranks, χ2 [df = 3,n = 172] = 9.91; P = 0.02; Table 3). However, post hoc pairwise, multiple-comparison, Tukey method procedures revealed no significant differencebetween phases for the reef site. The significant Kruskal-Wallis test


Table1.Detailsofdigital

acoustic

monitoring(D

MON)dep

loym

entsitesan

dreco

rdingtimes

ove

rthedurationofthestudy.

Site

Coordinates

Dep

loym

entdate

Recove

rydate

Dep

loym

entduration(tim

e)Recorded

minutes

Reef

44� 35.14

0N,12

4�07

.120

W16

May

2014

23May

2014

6d20

h00

min

985

44� 35.12

1N,12

4�07

.000

W12

Jun20

1420

Jun20

147d21

h30

min

1,13

844

� 34.98

5N,12

4�07

.117

W26

Jun20

147Jul20

1410

d14

h40

min

1,52

944

� 35.24

7N,12

4�06

.815

W29

Jul20

148Aug20

149d21

h40

min

1,42

744

� 35.22

1N,12

4�06

.859

W16

Sep20

1418

Sep20

142d13

h20

min

441

Offshore

44� 34.65

0N,12

4�13

.218

W12

Jun20

1420

Jun20

147d21

h40

min

1,13

844

� 34.85

7N,12

4�13

.419

W26

Jun20

147Jul20

1410

d23

h10

min

1,58

044

� 34.92

9N,12

4�13

.215

W29

Jul20

148Aug20

1410

d0h30

min

1,44

444

� 34.92

9N,12

4�13

.215

W16

Sep20

1429

Sep20

1412

d17

h40

min

1,83

544

� 34.92

9N,12

4�13

.215

W20

Sep20

1413

Oct

2014

12d12

h00

min

1,80

1


was likely due to a nearly significant difference between night and dayrevealed by the Tukey test (P = 0.05). At the offshore site, no significantdifference in PPMs across diel phase was detected (Kruskal–WallisANOVA on ranks, χ2 [df = 3, n = 228] = 4.15; P = 0.25). For BPMs, no vari-ation across the four diel phases was found at the reef site (Kruskal-WallisANOVA on ranks, χ2 [df = 3, n = 228] = 3.01; P = 0.39). However, at theoffshore site, the variation in BPMs showed significant variation acrossdiel phase (Kruskal-Wallis ANOVA on ranks, χ2 [df = 3, n = 228] = 29.775;P < 0.001); BPMs during the day was significantly lower than the BPMs inthe morning (P = 0.01) and night (P < 0.001) (Fig. 3).

Tidal Patterns

No significant pattern of PPM or BPM across tidal cycle was found atthe offshore site (Rayleigh’s test for circular uniformity, P = 0.2495, P =0.4065, respectively). However, at the reef site, PPM were significantlydifferent from a uniform distribution throughout the tidal phase(Rayleigh’s test for circular uniformity, P = 0.0078) with the peak inmean PPM occurring during late flood (1.97 radians) (Fig. 4). Despite avisual bimodal pattern, BPM at the reef site were found to be uniformlydistributed across the tidal phase (Rayleigh’s test of uniformity, P =0.5067) at the 95% confidence level. This results from a bimodal distri-bution with two peaks in BPM, observed at the reef site during the maxi-mum ebb (3.05 radians) and flood (1.470) phases of the tides.Therefore, a Hartigan’s dip test for unimodality was applied to test if thedistributions were unimodal or multimodal (i.e., at least bimodal, Harti-gan and Hartigan 1985). Values <0.05 indicate significant bimodality andvalues >0.05 and <0.10 suggest bimodality with marginal significance(Freeman and Dale 2013). The PPMs had a single mode evident(Hartigan’s dip test, P = 0.9539), whereas BPMs had a bimodal distribu-tion (Hartigan’s dip test, P = 0.05).

Figure 2. Percent of daily monitored minutes in which harbor porpoise weredetected for the reef and offshore sites throughout the study period. The grayshaded areas represent data gaps between deployments.


GAM Modeling

The final GAM with PPM as a binary response variable representingporpoise presence/absence at the reef site included Julian day, dielphase, tidal phase, the interaction between Julian day and diel phase,and the interaction between Julian day and tidal phase (Table 2). Thefinal GAM for PPM at the offshore site included the same variables asthe reef PPM except for the interaction between Julian day and tidalphase. Oddly, tidal phase was a significant temporal covariate for PPM

Figure 3. Percent of porpoise-positive minutes (PPM) that contained at leastfive click trains with minimum interclick intervals (MICIs) of < 10 ms, thusclassified as a buzz-positive minute (BPM). The star symbols and bracketsrepresent post hoc Tukey tests that gave significant results at the P < 0.05 level:Morning vs. Day and Day vs. Night for the offshore site.

Figure 4. Distribution of harbor porpoise acoustic activity at the reef sitemeasured as (a) porpoise positive minute (PPM) and (b) buzz positive minute(BPM) as a function of the tidal cycle. The length of the bars represents thebinned presence of PPM or BPM during a given tidal phase. The black arrowsrepresent the peak in mean PPMs and BPMs, respectively.


Table2.Recorded

datasetfortheen

tire

timeofinve

stigation,separated

foreach

stationbydielphasean

dtheirtotals.PPM:

porpoisepositive

minute;BPM:buzz-positive

minute.

Site

Diel

phase

Recorded

minutes

Porpoisepositive

minutes(PPM)

PPM

(%per

complete

observationperiod)

Clic

konly

minutes

Buzz

positive

minutes(B

PM)

BPM

(%per

PPM)

Reef

Morning

278

9233

.166

2628

.3Day

3,16

31,23

939

.287

636

329

.3Eve

ning

286

104

36.4

6836

34.6

Night

1,79

362

234

.743

618

629

.9Offshore

Morning

353

6217

.640

2235

.5Day

4,07

573

017

.959

313

718

.8Eve

ning

351

7822

.264

1417

.9Night

3,01

955

018

.237

018

032

.7


at the offshore site. It was found to be nonsignificant (P = 0.054) forPPMs at the reef site. However, at the reef there were more PPMs duringlow tide, while offshore had more PPMs during high tide. Diel phasewas significant at both sites, with more PPMs occurring during the dayat the reef site and during the evening and night at the offshore site. Thedeviance explained for the GAMs of PPM at the reef and the offshoresites, was 6.9% and 11.5%, respectively.The GAM of BPMs at the reef site included the same variables as the

GAM of PPMs at the reef site. However, diel phase was not significantfor foraging at the reef site, similar to the diel analysis. The final GAM ofBPMs at the offshore site included Julian day, diel phase, and their inter-action; no tidal phase parameters were included in the model for BPM.The deviance explained for the GAMs of BPM at reef site and the off-shore site was 13.7% and 13.2%, respectively.

DISCUSSION

This study provides first insights towards the fine scale spatial andtemporal patterns of habitat use by harbor porpoises off the central Ore-gon coast during the summer months. Our results indicate a regular useof our study area by harbor porpoises with almost daily presence at bothsites. Presumed feeding, i.e., buzzes, was detected in 27% of all PPMs,additionally highlighting this area as a regular feeding spot. Overall,echolocation activity indicative of presence and foraging at the reef sitewas higher, and likely reflective of prey availability. Harbor porpoiseforaging activity was also prevalent at the offshore site where feedingbuzzes were more correlated with diel patterns, whereas foraging activ-ity at the reef site was influenced by tidal phase. Harbor porpoises wererarely present at both the reef site and the offshore site at the same time.This may suggest that harbor porpoises in this region move between the

Table 3. Simultaneous DMON site deployment data: For the periods whenrecording devices were present at both the reef and offshore sites andsimultaneously recording, the number and percent of porpoise positive minutes(PPMs), and buzz positive minutes (BPMs) detected at each site are given, aswell as the number and percent (displayed in bold) that were detected at bothsites simultaneously during the same 10 min time period.

Detectiontype Location

Number ofdetections

Percent of totaldetection type

PPMs Reef 1,297 55.6%Offshore 514 22.0%Present at

both521 22.3%

BPMs Reef 546 68.6%Offshore 207 26.0%Present at

both43 5.4%


two nearby study sites. This temporal habitat use pattern may increaseforaging opportunities that are enabled by fine-scale oceanographic pat-terns driven by tidal and diurnal forces. More research is needed todescribe the mechanisms by which these environmental forces increaseprey availability for harbor porpoises at these two sites, but harbor por-poise spatial distribution appears to vary temporally relative to cycles(e.g., tidal phase, time of data) at small spatiotemporal scales (<10 km,hours).The location of these two deployment sites and their surrounding hab-

itat may offer an explanation for this temporal difference in space use.The reef site is located in a bathymetrically complex 30 m depth areaonly 4 km from shore, while the offshore site is 12 km from shore andin deeper (60 m) water over a flat sandy bottom. Tidal flows are likelymuch stronger at the reef site, due to the shallow nature and complexbathymetry in that area and proximity to the Yaquina River mouth.Whereas, in the open water of the offshore site, tidal flows are morereduced and likely do not have a strong impact on the environmentalconditions. The interaction of bathymetry and tides and subsequentattraction of cetaceans has also been demonstrated in the Bay of Fundy,Canada, where the movement of strong tidal flow around islands andacross variable bottom topography produced numerous fine scale tidalfronts and eddy systems (Smith et al. 1984). In our study, PPMs were

Table 4. Predictor environmental variables in generalized additive models(GAM) of harbor porpoise echolocation activity and their significance, withdeviance explained of entire model.

PredictorReefPPM

ReefBPM

OffshorePPM

OffshoreBPM

Julian day <0.001c <0.001c <0.001c <0.001c

Diel phase:Morning — — — —Day — — —Evening — — 0.003b —Night 0.01a — 0.04a <0.001c

Tidal phase 0.05 — 0.05 —Julian day × Dielphase:

Morning — — — <0.001c

Day — 0.04a <0.001c <0.001c

Evening <0.001c 0.001b <0.001c —Night <0.001c <0.001c <0.001c —Julian day × Tidalphase

0.02a <0.001c —

Diel phase × Tidalphase

— — — —

Deviance explained 6.9% 13.7% 11.5% 13.2%

aSignificant at the 0.05 probability level.bSignificant at the 0.01 probability level.cSignificant at the 0.001 probability level.


detected more often at the reef site during peak ebb and flood flow.Studies show that variables, such as tidal height, tidal speed, or tidalphase have a direct influence on the distribution (Marubini et al. 2009,Jones et al. 2014, Benjamins et al. 2016) and behavior (Johnston et al.2005, Pierpoint 2008) of porpoises. However, no consistent pattern hasemerged and it seems the preferred tidal height, speed or phase for por-poises is site specific. In agreement with our results, porpoises in Land’sEnd, Cornwall, U.K. and southwest Wales, U.K., were also found to pre-fer strong ebbing tidal flows for foraging (Pierpoint 2008, Jones et al.2014). Benjamins et al. (2016) hypothesized that peak tidal flows maydisrupt the ability for fish to stabilize their position in the water, there-fore creating opportunities for predators to take advantage of disor-iented prey. In addition, Jones et al. (2014) suggested porpoises hadadopted a foraging strategy of intercepting or “ambushing” prey duringebb tide that was concentrated on coastal and benthic topography. Stud-ies have also shown that porpoises are attracted to reef structures (Toddet al. 2009, Mikkelsen et al. 2013), likely due to increased prey. Takenas a whole, harbor porpoise may select a range of current and tidalregimes that interact with local topography to enhance relative vorticity(Johnston et al. 2005) and thus provide a consistent and predictable for-aging resource.Meanwhile, harbor porpoise at the offshore site displayed increased

feeding from sundown to sunrise. This is concurrent with other PAMstudies that reported porpoises appear to shift their distribution to dif-ferent depths and/or habitats at night, perhaps to take advantage ofchanging prey availability (Carlström 2005, Todd et al. 2009, Mikkelsenet al. 2013). Furthermore, water depth has a significant impact on por-poise diel rhythms, with more nocturnal porpoise echolocation activityoccurring in deeper waters (Brandt et al. 2014, Wisniewska et al. 2016).In deeper waters, porpoises may be feeding pelagically on prey speciesthat vertically migrate up into the water column at night, such as herring(Cardinale et al. 2003).Occurrence patterns of harbor porpoises suggest active selection of

habitat through tidal and diel mediated foraging intensity, likely due tomaximizing their foraging opportunities. This is supported by the highprobability of a harbor porpoise feeding event detected at either the reefor the offshore site (95%), compared to detection of a feeding event atboth sites during the same 10 min period (5%). The small percentage ofPPM and BPM at both sites simultaneously suggests this is the samepopulation of porpoises moving between the different foraging locationsdependent on diel or tidal forced environmental conditions that is likelylinked with prey availability at each site. The high energetic demandsand limited energy storage capacity of this species require them tospend a high proportion of their time foraging (Read and Gaskin 1985)and their ability to react to predictable drivers of prey can greatly reduceforaging costs. Porpoises worldwide are reasonably opportunistic intheir foraging ecology (Recchia and Read 1989) and feed on a diversityof both pelagic and demersal fish. Harbor porpoise in west coast watersof the United States are known to feed on cephalopods and shrimp butprefer schooling nonspiny fishes such as herring (Clupea pallasii), smelt


(Osmeridea spp.), mackerel (Trachurus symmetricus and Scomber japo-nicus), sardines (Sardinops sagax), pollock (Gadus chalcogrammus)and whiting (Merluccius productus) (Leatherwood and Reeves 1983). Inour study, we have no empirical assessment on harbor porpoise prey offthe Oregon coast. However, at least two prey species for porpoises (her-ring and whiting) that are found in this region are known to spend timenear the surface, especially during the night (Cardinale et al. 2003), andcould likely be the targeted prey offshore.The gradual increase of detections from May to June, and peak detec-

tions between summer and fall, observed in this study is consistent withthe hypothesis that harbor porpoises move nearshore in relation to largescale temperature changes, which may increase prey availability andmating and calving opportunities (Dohl et al. 1983, Green et al. 1992).Our results correspond to previous reports documenting the largest con-centrations of harbor porpoises along the west coast of the United Statesoccur in summer and early fall, specifically September (Calambokidisand Barlow 1987, Barlow et al. 1988). However, our temporal coverageis limited and constrains our ability to address this knowledge gap ofwinter distribution patterns.Our final GAM models had a relatively poor fit to the data the model

was built upon (<15%), suggesting that most of the variability drivingharbor porpoise distribution in this area is due to factors not measuredand included in our study. However, results from the GAMs that assessall three temporal variables (day of year, time of day, tidal phase param-eter) simultaneously provide largely comparable results to our analysisof the individual relationships between these factors and harbor por-poise presence. Due to the static nature of our recording stations, weare unable to incorporate spatial drivers of porpoise presence or behav-ior (i.e., temperature, productivity, salinity) and thus our models weresimply constructed using the available temporal variables. It is likely thatour models would improve with the inclusion of information ondynamic environmental conditions and the distribution and abundanceof prey species. Regardless, it is common for GAMs modeling cetaceanoccurrence at high resolution to explain only a small portion of the devi-ance (Best et al. 2012, Forney et al. 2012), and this is particularly truefor temporal models (de Boer et al. 2014, Temple et al. 2016, Wingfieldet al. 2017).Nevertheless, recognizing and understanding the temporal drivers of

harbor porpoise distribution can better inform the location and timingof management actions to allow more effective risk mitigation and popu-lation monitoring of harbor porpoise. Results from this study indicatethat presence and foraging behavior of harbor porpoises in this area isrelated to seasonal, diel and tidal factors relative to local habitat. Thesehighly resolve temporal patterns can only be achieved effectively byPAM. High frequency passive acoustic recording devices like DMONsoffer great promise for the study of the ecology, behavior, and conserva-tion of small, acoustically active cetaceans. However, recording at thesehigh frequencies is technologically challenging due to the accompanyingincrease in data storage requirements and power. While we were able tocapture valuable data on harbor porpoise presence, the limited battery


life and memory storage of the DMONs is particularly challenging forlong-term monitoring studies. Moreover, bottom-trawling and ship trafficin general produce large amounts of background noise and are a threatto moored devices. An ideal recording device for future studies shouldbe capable of capturing high frequency species repertoires with a longbattery life so that year round presence of harbor porpoises can bedetermined.This study begins to fill information gaps needed to understand the

temporal variations in harbor porpoise distribution and behavior inorder to apply effective spatial management and conservation strategies.This data set may serve as a baseline from which to identify criticalareas, refine current conservation efforts, and compare future trends formonitoring harbor porpoise off the Oregon coast. Continued andexpanded monitoring of porpoise occurrence and behavior will be a crit-ical component in future conservation efforts.

ACKNOWLEDGMENTS

This study was supported with funding from the U.S. Department of EnergyAward # DE-FG36-08GO18179-M001 and the Northwest National MarineRenewable Energy Center. Oregon Sea Grant provided support, guidance andexpertise that greatly assisted the research. We would like to thank the Captainand the crew of the R/V Elakha and all the volunteers who helped deploy andrecover the DMONS. We also thank Britney Honisch who conducted manyhours of visual data analysis. We are grateful to the anonymous reviewers andthe editor whose input greatly improved the manuscript. This paper is NOAA-PMEL contribution number 4599.

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Received: 6 March 2017Accepted: 18 May 2018


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